Cannabis intoxication has been associated with slowed response time, affecting performance at work or school, or impairing driving behavior leading to injuries or fatalities. Now cannabis intoxication can easily be detected, thanks to smartphone sensors.
According to a study, which evaluated the feasibility of using smartphone sensor data to identify episodes of cannabis intoxication in the natural environment, a combination of time features and smartphone sensor data had a 90% rate of accuracy.
Tammy Chung, professor of psychiatry and director of the Center for Population Behavioral Health, Rutgers Institute for Health, Health Care Policy and Aging Research, highlighted that using sensors in a person’s health, researchers might be able to detect when a person might be experiencing cannabis intoxication and deliver a brief intervention when and where it might have the most impact to reduce cannabis-related harm. Chung said existing detection measures such as blood, urine or saliva tests have limitations as indicators of cannabis intoxication and cannabis-related impairment in daily life.
The researchers, as per Science Daily, analyzed daily data collected from young adults who reported cannabis use at least twice per week. They examined phone surveys, self-initiated reports of cannabis use, and continuous phone sensor data to determine the importance of time of day and day of week in detecting use and identified which phone sensors are most useful in detecting self-reported cannabis intoxication.
They found that time of day and day of week had 60% accuracy in detecting self-reporting of cannabis intoxication and the combination of time features and smartphone sensor data had 90% accuracy in detecting cannabis intoxication. The researchers shared that travel patterns from GPS data, at times when they reported feeling high and movement data from accelerometer that detects different motions, were the most important phone sensor features of detection of self-reported cannabis intoxication.
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For the study, researchers used low burden methods (tracking time of day and day of week and analyzing phone sensor data) to detect intoxication in daily life and found that the feasibility of using phone sensors to detect subjective intoxication from cannabis consumption is strong.
